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PROTOTYPING

WindOW

WindOW

Team info

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Bose Sumantraa
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Karthik Subramanian Balasubramanian
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Ajay Balasubramaniam

Clusters

The challenge 

Operation and Maintenance (OPEX) costs of wind turbines currently account for 30% of the Levelized Cost of Wind Energy. It can be minimized mainly by solving two problems 1) Predicting failures well in advance to avoid unplanned repair costs, which is projected to account for 70% of all OPEX costs by 2028 2) Optimizing the turbine operation for maximum performance If these problems can be solved without installing additional sensors, the process of solving the problem itself does not incur additional costs and thus can help in significantly reducing the OPEX costs. Driving down the OPEX costs will help accelerate global wind energy transition.

The solution

Wind Data Analytics has been estimated to generate value of nearly 8000 $/turbine/year, with a payback period of 3 years. The proposed software solution employs state of the art data driven and physics based models to analyse historical and real time data from wind turbine controllers to improve performance, forecast production and ultimately move towards predictive maintenance. The proposed product offers real-time monitoring of the operational parameters and customized Key Performance Indicators such as validated Power Curve measurements to help in informed decision making. The user gets a dashboard in a user-friendly interface to know the current state of the turbine which includes power production, turbine availability, downtime, component health and production forecast

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